Iterative Process

Il crescente numero di incendi richiede una organizzazione difensiva più attenta alla prevenzione, affidando un ruolo fondamentale agli strumenti di indagine territoriale, che possono aiutare ad individuare linee di tendenza e concentrazione del fenomeno, a interpretarne la dinamica e le motivazioni e ad impostare efficaci azioni di previsione del rischio. Fondamentale appare l'impiego di tecniche di investigazione che consentono di definire la distribuzione spaziale degli eventi sul territorio, agevolando l'interpretazione di un fenomeno di origine prevalentemente antropica con l'aiuto della conoscenza del problema a livello locale e dell'esperienza degli operatori del settore AIB. Il lavoro affronta aspetti relativi alla distribuzione del fenomeno attraverso il metodo della kernel density e all'applicazione del metodo Delphi per la valutazione delle motivazioni. L'applicazione delle due metodologie di analisi in ambito provinciale ha permesso l'identificazione delle motivazioni principalmente di origine volontaria (ampliamento, apertura o rinnovazione del pascolo a spese del bosco) e involontaria (attività di ripulitura di incolti, bruciature stoppie, attività di miglioramento o rinnovazione del pascolo). Parole chiave: metodo Delphi; kernel density; motivazioni; industria del fuoco; processo interattivo; gruppo di esperti; incendi volontari.

This paper describes the application of a methodology for game genre and player experience innovation called "play-centric design." This method is shown in context as the primary design methodology for an experimental play project, Cloud, created by students from the USC School of Cinema-Television's Interactive Media Division. The application of the play-centric design method throughout the project is detailed and explored as a potential model for innovating in the realm of overall player experience as opposed to traditional research focus on technology or subject matter innovation.

In this paper we prove fixed point theorems for orderedĆirić-Prešić type contraction in cone metric spaces without assuming the normality of cone. These results generalize and extend the Banach contraction principle and several known results in ordered cone metric spaces. Some examples are presented to illustrate the cases when new results can be applied while old one can not.

The authors propose that conceptual and procedural knowledge develop in an iterative fashion and that improved problem representation is 1 mechanism underlying the relations between them. Two experiments were conducted with 5th-and 6th-grade students learning about decimal fractions. In Experiment 1, children's initial conceptual knowledge predicted gains in procedural knowledge, and gains in procedural knowledge predicted improvements in conceptual knowledge. Correct problem representations mediated the relation between initial conceptual knowledge and improved procedural knowledge. In Experiment 2, amount of support for correct problem representation was experimentally manipulated, and the manipulations led to gains in procedural knowledge. Thus, conceptual and procedural knowledge develop iteratively, and improved problem representation is 1 mechanism in this process. Understanding the process of knowledge change is a central goal in the study of development and education. Two essential types of knowledge that children acquire are conceptual understanding and procedural skill. Competence in domains such as mathematics rests on children developing and linking their knowledge of concepts and procedures (Silver, 1986). However, competing theories have been proposed regarding the developmental relations between conceptual and procedural knowledge. The majority of past research and theory on these relations has focused on whether conceptual or procedural knowledge emerges first (Rittle-Johnson & Siegler, 1998). The developmental precedence of one type of knowledge over another has been hotly

This paper describes the application of a methodology for game genre and player experience innovation called "play-centric design." This method is shown in context as the primary design methodology for an experimental play project, Cloud, created by students from the USC School of Cinema-Television's Interactive Media Division. The application of the play-centric design method throughout the project is detailed and explored as a potential model for innovating in the realm of overall player experience as opposed to traditional research focus on technology or subject matter innovation.

In this paper, we introduce an iterative process for finding the common element of the set of common fixed points of a countable family of nonexpansive mappings and the set of solutions of the variational inequality problem for an α-inverse-strongly-monotone mapping. We obtain a weak convergence theorem for a sequence generated by this process. Moreover, we apply our result to the problem for finding a common element of the set of equilibrium problems and the set of solutions of the variational inequality problem of a monotone mapping.

This paper describes a highly flexible computerized method of calculating operating data in a power cycle. The computerized method presented here permits the study of steam, gas and combined plants, Its flexibility is not restricted by any defined cycle scheme. A power plant consists of simple elements (turbine, compressor, combustor chamber, pump, etc.). Each power plant componment is represented by its typical equations relating to fundamental mechanical and thermodynamic laws, so a power plant system is represented by algebraic equations, which are the typical equations of components, continuity equations, and data concerning plant conditions. This equation system is not linear, but can be reduced to a linear equation system with variable coefficients. The solution is simultaneous for each component and it is determined by an iterative process. An example of a simple gas turbine cycle demonstrates the applied technique. This paper also presents the user interface based on MS-Wind...

Background . This study focuses on a simulation strategy that will allow the performance characteristics of an isolated gas turbine engine component, resolved from a detailed, high-fidelity analysis, to be transferred to an engine system analysis carried out at a lower level of resolution. This work will enable component-level, complex physical processes to be captured and analyzed in the context of the whole engine performance, at an affordable computing resource and time. Approach. The technique described in this paper utilizes an object-oriented, zero-dimensional (0D) gas turbine modeling and performance simulation system and a high-fidelity, three-dimensional (3D) computational fluid dynamics (CFD) component model. The work investigates relative changes in the simulated engine performance after coupling the 3D CFD component to the 0D engine analysis system. For the purposes of this preliminary investigation, the high-fidelity component communicates with the lower fidelity cycle ...

In this paper, we introduce an iteration scheme for two multivalued maps in Kohlenbach hyperbolic spaces. This extends the single-valued iteration process due to Agarwal et al. (J. Nonlinear Convex Anal. 8(1):61-79, 2007). Using this new algorithm, we approximate common fixed points of two multivalued mappings through -convergence and strong convergence under some weaker conditions. A necessary and sufficient condition is given for strong convergence. MSC: Primary 47A06; 47H09; 47H10; secondary 49M05

The existence of fixed points of single-valued mappings in modular function spaces has been studied by many authors. The approximation of fixed points in such spaces via convergence of an iterative process for single-valued mappings has also been attempted very recently by Kozlowski (Fixed Point Theory Appl. 2012:118, 2012). In this paper, we initiate the study of approximating fixed points by the convergence of a Mann iterative process applied on multivalued ρ-nonexpansive mappings in modular function spaces. Our results also generalize the corresponding results of (Dehaish and Kozlowski in Fixed Point Theory Appl. 2012Appl. :118, 2012 to the case of multivalued mappings. MSC: 47H09; 47H10; 54C60

Due to its many advantages, the switched reluctance machine (SRM) finds several applications such as in the high-speed motor operation or in the power generation. However, unlike many other electric machines, the SRM has the particularity of working in highly saturated regime, which leads to a nonlinear mathematical model. Therefore, this nonlinear behavior requires an iterative process for designing a given structure of machine. In continuation of the previous work carried out by our research team, LGEC, in Electrotechnics Dept.at the University of Constantine1 (ED-UC1), Algeria, we will present in this paper a hybrid methodology based on an analytical preliminary design of a doubly salient 6/4 SRM followed by a finite elements analysis (FEA) using FEMM and MATLAB/Simulink software.

In this paper, we study a nonlocal mixed problem for a nonlinear hyperbolic equation. Based on some a priori estimates and some density arguments, we prove the well posedness of the associated linear problem. The existence and uniqueness of the weak solution of the nonlinear problem are then established by applying an iterative process based on the obtained results for the linear problem. Copyright © 2009 John Wiley & Sons, Ltd.

Most improvements in software development technology have occurred by eliminating the accidental aspects of the technology. Further progress now depends on addressing the essence of software. Fred Brooks has characterized the essence of software as a complex construct of interlocking concepts. He concludes that no silver bullet will magically reduce the essential conceptual complexity of software. This paper expands on Brooks's definition to lay a foundation for forging a possible silver bullet. Discussed are the three essential attributes of software entities from which a number of consequences arise in software development: (1) conceptual content, (2) representation, and (3) multiple subdomalns. Four basic approaches to develop technologies are proposed that directly address the essential attributes. Although some of these technologies require additional development or testing, they present the most promise for forging a silver bullet. Among them, design reabstraction addresses the most difficult attribute, multiple subdomains, and the most difficult consequence, enhancing existing code, making It the best prospect.

An inverse problem of inhomogeneity identification inside a nonlinear magnetic material from the local measurements of the magnetic induction is investigated. The representation of the shape of the inhomogeneity and its evolution during an iterative reconstruction process is achieved by the level set method. The reconstruction is realized by the minimization of a cost function using the steepest descent method. The gradient directions are evaluated using the sensitivity equation and the adjoint variable method. Simulations has been performed showing the robustness of the algorithm and its ability to reconstruct single inhomogeneities, convex and non-convex, as well as multiple inhomogeneities.

A detailed analysis of convergence rate is presented for an iterative path formulated optimal routing algorithm. The primary objective is to quantify, analytically, how the convergence rate changes as the number of nodes in the underlying graph increases. The analysis is motivated by a particular path formulated gradient projection algorithm that has demonstrated excellent convergence rate-. properties through extensive numerical studies. In particular, the empirical data suggests that the number of iterations required for convergence to within a small fraction of the optimal cost is relatively independent of the number of nodes. Deriving a correspondingly tight analytical bound for the number of iterations required for convergence, as a function of problem size, proves to be a formidable task, primarily because the dimension of the underlying optimization problem (i.e., the total number of paths connecting all origin-destination pairs) generically grows with a super-polynomial function of the number of nodes. The main result of this paper is that the number of iterations for convergence depends on the number of nodes only through the network diameter. I. INTRODUCTION AND PROBLEM FORMULATION A. Basics The primary objective of virtually all routing algorithms is to select routes for those origin destination (OD) pairs that request data communication. A secondary objective is to insure that messages transmitted along the selected routes are delivered to the correct destinations. This latter objective is accomplished by using standard techniques involving protocols and routing tables. In this p q e r the focus is on the former objectivethe route selection problem. It is known that route selection has a substantial impact on the performance of data networks [l-9,12,13,15,16]. Roughly speaking, an optimal routing is a set of routes that yields the "best n network performance-based on some quantitative measure. The types of performance measures employed by most optimal routing formulations, estimate, in some sense, the average delay associated with sending a packet of data to a typical destination node. An important issue to consider when implementing a routing algorithm in a large distributed data network is the question of whether the computation should be done in a centralized or distributed manner. Centralized implementations are fairly straightforward: a designated "central" node is sent data (which characterizes the state of the network) from the other network nodes; then, based on this information, the central node solves the optimal routing problem and broadcasts the solution back out to the network. One of the obvious problems with this type of scheme is the associated communication overhead (i.e., bottleneck). In contrast, certain distributed implementations can reduce this communication overhead, by requiring, for example, only nearest neighbor communication. Due to the potentidy high degree of fault tolerance, fast convergence rates, low communication overhead, and other reasons, distributed implementations have received a great deal of attention in the literature over the past decade or so. One fundamental question associated with distributed routing algorithms is that of convergence. Namely, because the order of events in distributed algorithms occur asynchronously (to one degree or another), the question of whether the algorithm will converge becomes a non-trivial one. In references [5] and [6], convergence of a class of distributed optimd routing algorithms is proven under very mild assumptions. The present authors have proven convergence of a class of distributed iterative aggregation algorithms [15], which have applications in optimal routing.

In this paper we consider bilinear forms of matrix polynomials and show that these polynomials can be used to construct solutions for the problems of solving systems of linear algebraic equations, matrix inversion and finding extremal eigenvalues. An almost Optimal Monte Carlo (MAO) algorithm for computing bilinear forms of matrix polynomials is presented. Results for the computational costs of a balanced algorithm for computing the bilinear form of a matrix power is presented, i.e., an algorithm for which probability and systematic errors are of the same order, and this is compared with the computational cost for a corresponding deterministic method.

Il crescente numero di incendi richiede una organizzazione difensiva più attenta alla prevenzione, affidando un ruolo fondamentale agli strumenti di indagine territoriale, che possono aiutare ad individuare linee di tendenza e concentrazione del fenomeno, a interpretarne la dinamica e le motivazioni e ad impostare efficaci azioni di previsione del rischio. Fondamentale appare l'impiego di tecniche di investigazione che consentono di definire la distribuzione spaziale degli eventi sul territorio, agevolando l'interpretazione di un fenomeno di origine prevalentemente antropica con l'aiuto della conoscenza del problema a livello locale e dell'esperienza degli operatori del settore AIB. Il lavoro affronta aspetti relativi alla distribuzione del fenomeno attraverso il metodo della kernel density e all'applicazione del metodo Delphi per la valutazione delle motivazioni. L'applicazione delle due metodologie di analisi in ambito provinciale ha permesso l'identificazione delle motivazioni principalmente di origine volontaria (ampliamento, apertura o rinnovazione del pascolo a spese del bosco) e involontaria (attività di ripulitura di incolti, bruciature stoppie, attività di miglioramento o rinnovazione del pascolo). Parole chiave: metodo Delphi; kernel density; motivazioni; industria del fuoco; processo interattivo; gruppo di esperti; incendi volontari.

This paper is concerned with the equational logic of corecursion, that is of de nitions involving nal coalgebra maps. The framework for our study is iteration theories (cf. e.g. Bloom and Esik 2, 3]), recently re-introduced as models of the FLR 0 fragment of the Formal Language of Recursion 7,. We present a new class of iteration theories derived from nal coalgebras. This allows us to reason with a number of types of xedpoint equations which heretofore seemed to require metric or order-theoretic ideas. All of the work can be done using nality properties and equational reasoning.

In particle transport applications there are numerous physical constructs in which heterogeneities are randomly distributed. The quantity of interest in these problems is the ensemble average of the flux, or the average of the flux over all possible material ‘realizations.’ The Levermore-Pomraning closure assumes Markovian mixing statistics and allows a closed, coupled system of equations to be written for the ensemble averages of the flux in each material. Generally, binary statistical mixtures are considered in which there are two (homogeneous) materials and corresponding coupled equations. The solution process is iterative, but convergence may be slow as either or both materials approach the diffusion and/or atomic mix limits. A three-part acceleration scheme is devised to expedite convergence, particularly in the atomic mix-diffusion limit where computation is extremely slow. The iteration is first divided into a series of ‘inner’ material and source iterations to attenuate the diffusion and atomic mix error modes separately. Secondly, atomic mix synthetic acceleration is applied to the inner material iteration and S2 synthetic acceleration to the inner source iterations to offset the cost of doing several inner iterations per outer iteration. Finally, a Krylov iterative solver is wrapped around each iteration, inner and outer, to further expedite convergence. A spectral analysis is conducted and iteration counts and computing cost for the new two-step scheme are compared against those for a simple one-step iteration, to which a Krylov iterative method can also be applied.

Compressed sensing (CS) can be applied in distributed scenarios, where the objective is to independently compress several signals that are characterized by presenting a sparse correlation. In this case, the compressed version of each signal is produced without knowledge of the other signals. The decoder has access to the compressed versions of all the signals of interest and recovers them by exploiting the signal correlations. Motivated by the idea of incorporating prior information in distributed CS we propose to study the effects of including signal support correlation information in the reconstruction process. We investigate the performance improvement obtained by jointly recovering two correlated sources, compared to single source recovery, in terms of number of samples (measurements) required to encode the signal for successful recovery. To perform recovery, we modify the OMP algorithm to jointly recover two correlated sources with partially disjoint support. The final reconstruction algorithm is an iterative process that incorporates prior information of the sources, resembling joint source channel digital coding schemes, where probabilistic information is iteratively exchanged. The study is carried out by means of numerical simulations of synthetic signals with a probabilistic model.

We discuss in this paper heteroscedastic linear models with symmetrical errors. The symmetrical class includes all symmetrical continuous distributions such as normal, Student-t, power exponential, logistics I and II, contaminated normal, so on. The variety of error distributions with different kurtosis coefficients than the normal one may give more flexibility in the choice of an appropriate error distribution, particularly to accommodate outlying and influential observations. We derive a joint iterative process for estimating the location and dispersion coefficients and we discuss some robustness aspects of the maximum likelihood estimates against outlying and large variance observations. The score test proposed by Diagnostics for heteroscedasticity in regression. Biometrika 70, 1-10] is generalized and some diagnostic procedures such as leverage, local influence and residual analysis are derived. Finally, a data set is analyzed under heteroscedastic linear models with normal and heavy-tailed error distributions. r

Compressed sensing (CS) can be applied in distributed scenarios, where the objective is to independently compress several signals that are characterized by presenting a sparse correlation. In this case, the compressed version of each signal is produced without knowledge of the other signals. The decoder has access to the compressed versions of all the signals of interest and recovers them by exploiting the signal correlations. Motivated by the idea of incorporating prior information in distributed CS we propose to study the effects of including signal support correlation information in the reconstruction process. We investigate the performance improvement obtained by jointly recovering two correlated sources, compared to single source recovery, in terms of number of samples (measurements) required to encode the signal for successful recovery. To perform recovery, we modify the OMP algorithm to jointly recover two correlated sources with partially disjoint support. The final reconstruction algorithm is an iterative process that incorporates prior information of the sources, resembling joint source channel digital coding schemes, where probabilistic information is iteratively exchanged. The study is carried out by means of numerical simulations of synthetic signals with a probabilistic model.

In this paper, we introduce an iteration scheme for two multivalued maps in Kohlenbach hyperbolic spaces. This extends the single-valued iteration process due to Agarwal et al. (J. Nonlinear Convex Anal. 8(1):61-79, 2007). Using this new algorithm, we approximate common fixed points of two multivalued mappings through -convergence and strong convergence under some weaker conditions. A necessary and sufficient condition is given for strong convergence. MSC: Primary 47A06; 47H09; 47H10; secondary 49M05

This paper presents engineering decision-making on pipe stress analysis through the application of knowledge-based systems (KBS). Stress analysis, as part of the design and analysis of process pipe networks, serves to identify whether a given pipe arrangement can cope with weight, thermal, and pressure stress at safe operation levels. An iterative process of design and analysis cycle is done routinely by engineers while analyzing the existing networks or while designing the process pipe networks. In our proposal, the KBS establishes a bidirectional communication with the current engineering software for pipe stress analysis, so that the user benefits from this integration. The stress analysis knowledge base is constructed by registering the senior engineers’ know-how. The engineers’ overall strategy to follow up during the pipe stress analysis, to some extent contained by the KBS, is presented. Advantages in saving engineering man-hours and usefulness in guiding experts in pipe stress analysis are the major services for the process industry.

The transient extensional rheology and the dynamics of elastocapillary thinning in aqueous solutions of polyethylene oxide (PEO) are studied with high-speed digital video microscopy. At long times, the evolution of the thread radius deviates from self-similar exponential decay and competition between elastic, capillary and inertial forces leads to the formation of a periodic array of beads connected by axially-uniform ligaments. This configuration is unstable and successive instabilities propagate from the necks connecting the beads and ligaments. This iterated process results in multiple generations of beads developing along the string in general agreement with predictions of Chang et al. [Phys Fluids, 11, 1717] although the experiments yield a different recursion relation between the successive generations of beads. At long times, finite extensibility truncates the iterated instability, and slow axial translation of the bead arrays along the interconnecting threads leads to progressive coalescence before the ultimate rupture of the fluid column. Despite these dynamical complexities it is still possible to measure the steady growth in the transient extensional viscosity by monitoring the slow capillarydriven thinning in the cylindrical ligaments between beads.

The main objective of this paper is to relieve the power system engineers from the burden of the complex and time-consuming process of power system stabilizer (PSS) tuning. To achieve this goal, the paper proposes an automatic process for computerized tuning of PSSs, which is based on an iterative process that uses a linear matrix inequality (LMI) solver to find the PSS parameters. It is shown in the paper that PSS tuning can be written as a search problem over a non-convex feasible set. The proposed algorithm solves this feasibility problem using an iterative LMI approach and a suitable initial condition, corresponding to a PSS designed for nominal operating conditions only (which is a quite simple task, since the required phase compensation is uniquely defined). Some knowledge about the PSS tuning is also incorporated in the algorithm through the specification of bounds defining the allowable PSS parameters. The application of the proposed algorithm to a benchmark test system and the nonlinear simulation of the resulting closed-loop models demonstrate the efficiency of this algorithm.

In this paper we deal with performance analysis of Monte Carlo algorithm for large linear algebra problems. We consider applicability and efficiency of the Markov chain Monte Carlo for large problems, i.e., problems involving matrices with a number of non-zero elements ranging between one million and one billion. We are concentrating on analysis of the almost Optimal Monte Carlo (MAO) algorithm for evaluating bilinear forms of matrix powers since they form the so-called Krylov subspaces. Results are presented comparing the performance of the Robust and Non-robust Monte Carlo algorithms. The algorithms are tested on large dense matrices as well as on large unstructured sparse matrices.

Technology scaling will soon enable high-performance processors with hundreds of cores integrated onto a single die, but the success of such systems could be limited by the corresponding chip-level interconnection networks. There have been many recent proposals for nanophotonic interconnection networks that attempt to provide improved performance and energy-efficiency compared to electrical networks. This paper discusses the approach we have used when designing such networks, and provides a foundation for designing ...

Les méthodes de Conception Centrée Utilisateurs (CCU) et de développement agile d'applications partagent à la fois l'objectif ultime de satisfaction maximum des utilisateurs finaux et l'utilisation d'un processus itératif de conception. Cependant l'agilité se centre principalement sur les aspects fonctionnels du produit adressant ainsi en partie la notion d'utilité, tout en faisant abstraction des questions d'utilisabilité. Dans cet article, nous proposons une méthodologie de gestion de projet de développement visant à concilier utilisabilité et fonctionnalités du produit final via l'utilisation de la CCU et des méthodes agiles.

The study attempts to seek the optimal thermal design of planar multichip module (MCMs) under natural convection through optimal chip placement design. To attain the goal, a sequential metamodeling-based optimization approach is introduced. This approach incorporates a response surface methodology (RSM)-based design of experiment (DOE), three-dimensional (3D) thermal finite element analysis (FEA) and an updating scheme. Essentially, the RSM is used to construct, via quadratic polynomial approximation, the global RS of the chip junction temperature in terms of design variables. For speeding up the DOE and the solution of the optimization, several dynamic experimental design strategies using move limits and different proposed sampling techniques are introduced. The feasibility of the strategies is demonstrated, and their solution accuracy and efficiency are also compared with each other. By the explicit RS-based performance function together with geometry constraints, a constrained thermal optimization subproblem is formed. The optimum of the subspace optimization is sought, which is considered as the nominal starting point of next iteration. The iterative process continues with a new defined design subspace and factorial design plan until convergence is attained. The applicability of the proposed design optimization technique is demonstrated through several design case studies involving various planar MCMs.

In this paper, we introduce a new one-step iterative process to approximate common fixed points of two multivalued nonexpansive mappings in a real uniformly convex Banach space. We establish weak and strong convergence theorems for the proposed process under some basic boundary conditions.

This paper presents a new method for the simultaneous estimation of lighting direction and shape from shading. The method estimates the shape and the lighting direction using a two step iterative process. We assume an initial (possibly incorrect) estimate of the lighting position. A stiff deformable model is then fitted to the image, assuming this lighting position. Next, a least-squares estimate of the lighting position is derived from the model using the Levenberg-Marquart method. The two steps-model fitting and lighting-position estimation-are iterated. Once the light direction has converged to a stable solution the deformable model stiffness is lowered and the model fits accurately given the lighting model. In addition, we show how the method can be used with either orthographic or perspective projection assumptions. In a variety of experiments on real and synthetic data, the method is robust to errors both to the initial light position and shape estimates.

RESUMEN: Este artículo describe el problema general de agrupamiento, particularmente aquel en el que se busca conformar grupos de igual tamaño y equitativos respecto a más de un atributo, como un problema de optimización multiobjetivo, cuya solución por medio de una búsqueda exhaustiva no siempre será conveniente dada la explosión combinatoria que puede presentarse. Como alternativa a esta situación, se propone un método basado en algoritmos genéticos donde las soluciones posibles se codifican en estructuras tipo cromosoma a manera de matrices y donde por medio de un proceso iterativo en el que intervienen los operadores genéticos de selección, cruce y mutación, se guía el proceso de búsqueda hasta dar con una solución satisfactoria.

The aim of this article is to study an implicit iteration process with errors for a finite family of non-Lipschitzian asymptotically nonexpansive mappings in the intermediate sense in Banach spaces. Also we establish some strong convergence theorems and a weak convergence theorem for said scheme to converge to a common fixed point for non-Lipschitzian asymptotically nonexpansive mappings in the intermediate sense. The results presented in this paper extend and improve the corresponding results of [1], [3]-[8], [10]-[11], [13]-[14], [16] and many others.

In this paper, we consider a generalized iterative process with errors to approximate the common fixed points of two asymptotically quasi-nonexpansive mappings. A convergence theorem has been obtained which generalizes a known result. This theorem has then been used to prove another convergence theorem which, in turn, generalizes a number of results.